Delinquency-moving matrices for visualizing loan collections

ABSTRACT

The present invention, in one aspect, relates to tools for forecasting cash flow and income from a loan portfolio that are particularly useful in volatile markets. In one specific embodiment, consumer payment behavior is modeled, and account movement is simulated. For each month, actual payment amounts can be compared to delinquency, and frequency of payment can be compared to delinquency. Actual performance is then applied to current contractual payments for forecasting. In addition, the models facilitate determination of where payments are coming from, i.e., who is paying.

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. ProvisionalApplication No. 60/173,579, filed Dec. 29, 1999, which is herebyincorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

[0002] This invention relates generally to loans, and more specifically,to managing such loans, both collateral based and non-collateral based,including predicting cash inflow, expenses and income.

[0003] Determining whether to acquire a portfolio of collateral basedloans requires determining a value of the portfolio. The portfoliotypically has an initial value, and over a period of time, e.g., 5years, most all of the loans should be paid off. The value of theportfolio is directly related to how quickly the loans will be paid off,i.e., cash flow, and how much income will be generated from theportfolio.

[0004] The analysis required in order to determine the initial value,cash flow, and income can be difficult and tedious. For example, eachloan must be analyzed and information regarding each borrower (e.g.,payment history) must be considered in determining whether, or howlikely it is, that the borrower will make a payment in any given month.In a loan portfolio including several thousand loans, such analysis istime consuming.

[0005] Also, in volatile markets which are not very predictable and inwhich less than complete data is available, predicting collections ofloans is more difficult than in more stable markets. Collateral basedloan portfolios, however, sometimes become available for purchase inmore volatile markets. Using models for stable markets in valuing adistressed portfolio in a volatile market may result in significantlyover-valuing the distressed portfolio.

BRIEF SUMMARY OF THE INVENTION

[0006] The present invention, in one aspect, relates to tools forforecasting cash flow and income from a collateral based loan portfoliothat are particularly useful in volatile markets. In one specificembodiment, consumer payment behavior is modeled, and account movementis simulated for distressed assets. For each month, actual paymentamounts are analyzed by delinquency, and frequency of payment isanalyzed by, for example, asset type and region for delinquency. Actualperformance is then applied to current contractual payments forforecasting thereby allowing for changing assumptions (i.e. goingforward). In addition, the models facilitate determination of wherepayments are coming from, i.e. (who is paying).

BRIEF DESCRIPTION OF THE DRAWINGS

[0007]FIG. 1 illustrates a collections model;

[0008]FIG. 2 illustrates a re-marketing model;

[0009]FIG. 3 illustrates a re-marketing model including assumptions;

[0010]FIG. 4 is a portion of an exemplary work sheet for predictingdelinquency; and

[0011]FIG. 5 is a system diagram.

DETAILED DESCRIPTION OF THE INVENTION

[0012] The present invention is described herein in the context ofcollateral-based loans, and sometimes is described specifically withrespect to automobile based loans. The present invention, however, canbe utilized in many different contexts for other types of loans.Therefore, it should be understood that the present invention is notlimited to practice with automobile based loans, or collateral-basedloans.

[0013] In addition, the models described herein are particularly usefulin volatile markets for managing distressed loan portfolios. Models thathave been developed for more stable markets where more complete data isavailable are certainly suitable for such stable markets. However, andas explained above, such models are not necessarily applicable todistressed loan portfolios in volatile markets.

[0014] Volatile markets are markets which reflect changes in loandelinquencies based on one or more event occurances. Examples wouldinclude changes in the political climate, large interest rate changesand natural disasters. The models are flexible enough to calculateprobabilities of such events and predict results. For example, in a autoloan scenario, if a natural disaster such as a flood occurred, somenumber collateral assets are likely to be lost, thereby resulting inboth a decrease in loan collections and a rapid depreciation of thevehicles affected.

[0015] Other events are also incorporated into the models, for example,where the collateral is located affects speed of depreciation and costof repossession. In addition, all of the above are used in determiningcollection employee workload, since the model is used to predict whenand where delinquent accounts are likely to occur.

[0016] Further, the models and work sheets described herein can bepracticed on many different computer systems. For example, the modelsand work sheets can be implemented on a lap top computer including aPentium II processor. The particular computer on which the models andwork sheets are implemented can be selected based on the processingspeed desired, as well as the memory space needed due to the size of theportfolio and the extent of the models and work sheets to be generated.

[0017] With respect to understanding a portfolio of collateral basedloans, it is desirable to understand where the payments are being made,to project future inventory trends, and to visualize changes indelinquency and predict event occurances. The present invention meetseach of these needs. Specifically, tools for forecasting timing of cashinflow from a collateral-based loan portfolio, including modelingconsumer payment behavior and account movement, are described. Using thetools described herein, and each month, actual payment amount can becompared to delinquency, and frequency of payment can be compared todelinquency. Actual performance is then applied to current contractualpayments in order to predict cash inflow.

[0018] More specifically, and in one embodiment, a collateral-based loanmanagement system includes a collections model and a re-marketing model.A collections model 10 is illustrated in FIG. 1. Collections model 10sometimes is referred to as a “recency model” since model 10 providesdata by looking at a last payment date to predict future paymentbehavior.

[0019] As shown in FIG. 1, collections model 10 includes a category ofloans defined as “monthly contractual” 12. Such loans are ones in whicha monthly payment is due, e.g., a monthly payment for an automobileloan. Another category of loans is defined as “deemed uncollectable” 14.Such loans are “monthly contractual” loans in that no payment isexpected, one example is very delinquent loans. Although thedescriptions herein use “monthly contractual” 12 loans to describe model10, model 10 is not so limited. Model 10 is also used to predict paymentbehavior using delinquency measures, including, but not limited to,contractual, trailing 90 days (which is looking at only three months ofbehavior at one time, and for each month forward, looking at the mostrecent three months of data), trailing 180 days (same as trailing 90days, but with six month periods), and last payment date.

[0020] Within “monthly contractual” loans 12, there are loans which, forany particular month, multiple payments 16 have been made, one payment18 has been made, and no payment 20 has been made. Multiple payments maybe made, for example, if a loan customer has not made a payment formultiple past months and then submits a payment for more than one month.One payment may be made, for example, by customers who are current ontheir loan payments. Alternatively, one payment may be made by someonewho has not made payments for multiple past months and then submitspayment for one month. Such an account is deemed irregular or sporadic.The “no payment” category refers, of course, to customers who make nopayment during that particular month. Each monthly contractual type loanthat is not “deemed uncollectable” can be grouped in one of thecategories as shown in FIG. 1.

[0021] When a particular loan portfolio is acquired, the acquisition ofthe loans by a new loan manager can result in customers starting to payon loans that are then delinquent. Therefore, in determiningdelinquency, the number of days delinquent may be determined from thedate of the acquisition of the portfolio by the new loan manager.Alternatively, the delinquency may be determined simply based on thecontract terms and when the last payment was made.

[0022] Collections model 10 is used to forecast monthly cash inflow bypredicting, for example, consumer payment behavior based on historicalinformation combined with assumptions about potential changes in thefuture. Such prediction is based on payment amount versus contractualdelinquency, and measuring frequency of payment by delinquency.

[0023] In one specific embodiment, delinquency is determined for eachaccount. In the one specific embodiment, accounts that are 0-12 monthsdelinquent are categorized by delinquency (e.g., a separate category isprovided for each of month 0 through month 12). For accounts greaterthan 18 months delinquent, such accounts are captured in one category,i.e., >18 months delinquent. Each category is sometimes referred toherein as a “bucket”. For example, if there are 1,000 loans that are 3months delinquent, then there are 1,000 loans in the 3 month delinquentbucket.

[0024] The contractual obligations for each bucket are then determined.For example, for the 3 month delinquent bucket, there may be a total of$1,000,000 in payments that are contractually due for one month. Theamortization rate also is determined for each bucket. Amortization rateschange for each month as behavior changes. For example, customer loansare rolling forward and rolling back, thus the customers in each bucketchange from month to month, as does behavior, changes to theamortization rates reflect changes in customer behavior.

[0025] Each prior month performance is then analyzed on an account byaccount basis and grouped by asset type. Then, payments are compared tocontractual obligation and to projected amount grouped by bucket. Recentperformance is then compared to prior performance, and in oneembodiment, greater weights are assigned to recent performance. Changesin performance are reported to management to allow for changes incollection strategies. The assumptions for collections are then appliedfor expectations on future performance. A model reflecting the newinformation can then be generated to predict future cash flow.

[0026]FIG. 2 illustrates a re-marketing model 50. Model 50 is based onthose loans “deemed uncollectable” 14 in collections model 10. Oncedeemed uncollectable 14, then repossession of the collateral 52 againstwhich the loan is secured is pursued, e.g., repossess an automobile thatis collateral for an automobile loan. With respect to automobiles, orvehicles, such vehicles generally are within two categories. That is,either the vehicle is located 54 or the vehicle is not found 56. If thevehicle is located 54, then the vehicle can be auctioned 58, redeemed60, or placed in inventory 62. If the vehicle is not found 56, then anoutside agency 64 can be engaged to locate the vehicle or the particularloan can be written-off 66.

[0027] Re-marketing model 50 is particularly useful in capacityplanning. For example, and with respect to vehicles, if a large numberof vehicles are to be repossessed, then planning must be done to storesuch vehicles as well as to sell the vehicles. In addition, model 50 canbe utilized in forming a basis for predicting the value of vehicles tobe repossessed and sold, as well as the timing of such activities.

[0028] Modeling loan information using collections model 10 andre-marketing model 50 described above facilitates an understanding ofthe portfolio and assists in visualizing delinquencies. In addition tothe models described above, a spread sheet format can be utilizedvisualize other meaningful data associated with a loan portfolio, e.g.,to determine predictability of collections.

[0029]FIG. 3 illustrates exemplary assumptions for re-marketing. Similarassumptions are assigned for collections model, and the assumptionsillustrated in FIG. 3 are only by way of example. More specifically, andreferring to FIG. 3, it is assumed that 0-5% of all loans 12 will bedeemed for repossession 52. Vehicles will be located 54 for 60% of theloans for repossession 52, and 40% of the vehicles will not be found 56.With respect to the vehicles located 54, 0-10% will be auctioned 58, 5%will be redeemed 60, and 85-90% will go into inventory 62. Of thosevehicles to be auctioned 58, 20-60% will actually be auctioned, and ofthose vehicles to be redeemed 60, 20-70% will actually be redeemed.

[0030] With respect to the vehicles not found 56, in one embodimentabout 25% will be assigned to an outside agency for location 64, andabout 75% will be written off 66. Of those vehicles assigned to anoutside agency 64, 15% will actually be found. Again the percentagevalues used herein are those used in one embodiment and are forillustration only.

[0031] The assumptions represented in FIG. 3 can be updated from monthto month to more accurately reflect current data. The percentageassumptions described above are one example only and percentages changebased on other external factors. Using the assumptions, cash flowpredictions can be made based on those vehicles deemed for repossession52. A similar process is followed with respect to collections to make atotal cash flow estimate for a particular month.

[0032]FIG. 4 is a portion of an exemplary work sheet 100 for predictingdelinquency. Although delinquency work sheet 100 is described herein indetail, similar work sheets can be generated for other information ofimportance such as gross value, stock (i.e., book value of vehicles tobe repossessed), roll forward, roll back, payoff, and payment. Rollforward means an account that will become delinquent, or one more monthdelinquent, due to failure to make a payment. Roll back means that apayment is made on an overdue payment, e.g., if three months delinquentand make two payments (i.e., the current payment and one delinquentpayment), then this represents a roll back of one.

[0033] Exemplary work sheet 100 shown in FIG. 4 is generated usingMicrosoft Excel, Access, and Crystal Ball Application, which is used inthe Excel environment to assign probability distributions to theassumptions. Of course, other commercially available software can beutilized in generating such work sheets.

[0034] Referring now specifically to FIG. 4, and in a first column 102,percentages are shown, i.e., A %, B %, C %, D %. These percentagesindicate a probability that an account will roll forward into a nextmonth of delinquency. A second column 104 indicates the number of monthsthat an account is delinquent. For example, the “0” column is foraccounts that are zero months delinquent, and the “1” column is foraccounts that are one month delinquent. Delinquency is captured in thisformat for each month 106.

[0035] By capturing delinquency in this manner, a prediction can be madewith respect to which accounts, or buckets, by value will roll forwardinto the next bucket of delinquency. In addition, the assumptions forany particular month can be adjusted to account for variations due, forexample, to seasonal behavior. For example, in some countries, it may behighly unlikely that many payments will be received during hurricaneseason. The assumptions, or probabilities, can be adjusted to reflectthis seasonal variability.

[0036] Contrasted to a calculation of a gross roll rate, which iscalculated by looking at an entire portfolio for total delinquency (invalue or other units) for a period, for example, 60 days late in monthB, and determining what percentage of those delinquencies are carriedover from 30 days late in month A. The problem with such a determinationof roll rate is that it does not take into account delinquencies thatmay have been more than 90 days delinquent, but have applied payments toget those accounts paid up to where they are now only 60 days late.

[0037] Roll rate as used herein is calculated by a determination of thevalue of each loan, in aggregate, that has rolled forward from, forexample, 30 days delinquent to 60 days delinquent, that is, determiningthose accounts that did not pay. Alternatively, some accounts may rollback, that is, a 90 day delinquent loan may receive two payments in amonth, thereby rolling back to 60 days late. Determination of roll backand roll forward help in aligning collectors and collection efforts byusing model 10, to predict which buckets accounts will be in. Predictionof which buckets accounts will be located, allows allocation ofcollectors for each level of delinquency and allows focus of collectionefforts as continued deterioration of the portfolio occurs.

[0038] Again, and as explained above, work sheet 100 shown in FIG. 4 isan exemplary work sheet for predicting delinquency, and similar worksheets can be generated for other information of importance such asgross value, stock (i.e., book value of vehicles to be repossessed),roll forward, roll back (e.g., roll back 1, roll back 2, roll back 3,payoff), and payment. These work sheets facilitate visualizingdelinquency as well as cash flow and income.

[0039] Work sheet 100, as well as other work sheets which can begenerated in a similar manner as described above, are sometimes referredto as delinquency-moving matrices. Use of such delinquency-movingmatrices facilitates a better understanding of a portfolio and timing asto when payments will be made, i.e., cash inflow. In addition, aninitial portfolio value can be easily generated by summing the matricesfor collections, re-marketing, losses, and outstanding amounts due.

[0040] Further, and as shown with respect to work sheet 100, rather thanlooking at an entire portfolio in the aggregate to determine roll rate,with work sheet 100, roll rate is determined based on the behavior ofeach account on an account-by-account level. Such a more granularapproach to roll rate facilitates more accurate estimates with respectto payments, and also facilitates a better understanding as to where thepayments are coming from.

[0041] Also, the collection model described herein captures multiplepayments that may be made on a delinquent account. By capturing the factthat some borrowers may make multiple payments, a more accurate rollrate can be determined, rather than using a gross roll rate as definedabove. The model also is configurable to take into account other factorsor discrete events which affect payment behaviors. For example, duringholiday periods, collections may be only about 95% of normal. thereduction in collections are due to multiple factors includingcollectors taking holiday and more accounts not paying. Other eventsinclude, but not limited to, tax incentives which may alter consumerbehavior or a political event that may impact the portfolio in thefuture.

[0042]FIG. 5 illustrates an exemplary system 110 in accordance with oneembodiment of the present invention. System 110 includes a computerconfigured as a server 112 and a plurality of other computers 114coupled to server 112 to form a network. In one embodiment, computers114 are client systems including a web browser, and server 112 isaccessible to computers 114 via the Internet. In addition, server 112 isa computer. Computers 114 are interconnected to the Internet throughmany interfaces including a network, such as a local area network (LAN)or a wide area network (WAN), dial-in-connections, cable modems andspecial high-speed ISDN lines. Computers 114 could be any device capableof interconnecting to the Internet including a web-based phone or otherweb-based connectable equipment, including wireless web and satellite.Server 112 includes database 116 containing loan portfolios and accountpayment and delinquency information and is further configured to receiveand store information regarding loan collection modeling describedabove. Server 112 can be accessed by users at one of computers 114 bylogging onto server 112 through one of computers 114.

[0043] Although the invention has been described and illustrated indetail, it is to be clearly understood that the same is intended by wayof illustration and example only and is not to be taken by way oflimitation. Accordingly the spirit and scope of the invention are to belimited only by the terms of the appended claims and their equivalents.

What is claimed is:
 1. A method for visualizing loan collections, said method comprising the steps of: generating delinquency moving matrices; and predicting which accounts will roll forward into a next classification of delinquency.
 2. A method according to claim 1 wherein said step of generating delinquency moving matrices further comprises the step of assigning probability distributions to loan delinquency assumptions.
 3. A method according to claim 2 wherein said step of assigning probability distributions to loan delinquency assumptions further comprises the step of determining a percentage of loans within the probability distributions that will roll forward into a next period of delinquency.
 4. A method according to claim 3 further comprising the step of indicating a number of months an account is delinquent.
 5. A method according to claim 1 wherein said step of generating delinquency moving matrices further comprises the step of adjusting loan assumptions to account for variations based on external forces.
 6. A method according to claim 5 further comprising the step of adjusting probability distributions based on loan assumption adjustments.
 7. A method for visualizing loan collection data, said method comprising the steps of: generating matrices for at least one of delinquency, gross value, stock value, roll forward, roll back, amounts due and payment; and predicting a portfolio value using the matrices.
 8. A method according to claim 7 wherein said step of predicting a portfolio value further comprises the step of predicting a cash flow value for a portfolio.
 9. A system for visualizing loan collections, said system comprising: a computer configured with a delinquency moving model, said delinquency moving model configured to generate delinquency moving matrices and predict which accounts will roll forward into a next classification of delinquency.
 10. A system according to claim 9 wherein said model configured to assign probability distributions to loan delinquency assumptions.
 11. A system according to claim 10 wherein said model configured to determine a percentage of loans within the probability distributions that will roll forward into a next period of delinquency.
 12. A system according to claim 11 wherein said model configured to indicate a number of months an account is delinquent.
 13. A system according to claim 9 wherein said model configured to adjust loan assumptions to account for variations based on external forces.
 14. A system according to claim 13 wherein said model configured to adjust probability distributions based on loan assumption adjustments.
 15. A system in accordance with claim 9 wherein said computer further configured as a server, said system further comprising: at least one computer; and a network connecting said server to said at least one computer.
 16. A system according to claim 15 wherein said network is at least one of a WAN or a LAN.
 17. A system for visualizing loan collection data, said system comprising: a server; at least one computer; and a network connecting said server to said at least one computer, said server configured to: generate matrices for at least one of delinquency, gross value, stock value, roll forward, roll back, amounts due and payment; and predict a portfolio value using the matrices.
 18. A system according to claim 17 wherein said server configured to predict a cash flow value for a portfolio. 